Measurement in Health Psychology Research and Practice

by

Marie Johnston, John Weinman, Gozde Ozakinci

• LAST REVIEWED: 12 January 2021
• LAST MODIFIED: 12 January 2021
• DOI: 10.1093/obo/9780199828340-0270

Introduction

Health psychology is the investigation of psychological and behavioral processes in health, illness, and health care. It investigates the behavior of people who are healthy or ill as well as practitioners and policymakers delivering or making decisions that influence health care. Measures may be used in studies aiming to elucidate and understand processes to advance theory and evidence, or to gain greater understanding or even intervening to improve health and health-care outcomes. A wide range of research designs are used including observational, intensive longitudinal (or ecological momentary assessment [EMA]), experimental laboratory, pre-post event or intervention, and randomized controlled trials. Frequently investigations measure a target behavior or psychological state and examine (a) whether it can be predicted or influenced by antecedents (events, environments, cognitions, interventions) and (b) whether it predicts or influences consequences, especially health/illness outcomes. Health psychology shares measurement issues that are challenging for psychology in general, including establishing reliability and validity, scaling mental processes, and reactivity of measures. Additionally, self-report measures are frequently used to assess symptoms such as pain or reports of health behaviors such as smoking, and these assessments may be used to assess a clinical condition and to guide clinical decisions. They may be validated against an objective measure, but the objective measure may fail to represent the full range, context, or occurrence of the target construct and can therefore only give an indication rather than a solid assessment of validity of the self-report. Measures are often used to measure change over time, especially change associated with an intervention or a changing clinical state, and therefore need to be repeatable while retaining meaning; sensitivity to change may be more important than test-retest reliability. Longitudinal measurement has been aided by the use of digital mobile technologies, including wearables and automatic measurement of physiological processes. Additionally, physiological functioning, especially autonomic, immune, and cardiac, are commonly measured. Data collected routinely by health-care organizations such as attendance at appointments or health outcomes may be useful, but these data may be incomplete or only give indirect evidence of the target behaviors or outcomes. Finally, the context of measurement may determine what is possible: ill patients could be burdened by a long questionnaire; health-care professionals may have limited time and opportunity to respond to assessments and measures may need to be adapted for a clinical environment. The following sections address measures of key psychological constructs, health-related behaviors, and responses to health, illness, and health care.

General Overviews

Measurement in health psychology faces many of the challenges of measures in other areas of psychology. APA 2014 standards provide general guidance to good measurement and Fried and Flake 2020 discusses measurement issues that matter throughout psychology. Smith 2011 and Johnston, et al. 2016 provide general reviews of measurement issues in health psychology, while Streiner, et al. 2015 discusses methods and issues in health measurement. The Grid-Enabled Measures Database (GEM), the Patient-Reported Outcomes Measurement Information System (PROMIS), and the Science of Behavior Change (SOBC) offer excellent online resources with GEM and PROMIS being more extensive but SOBC giving more detailed evaluation of measures. McDowell 2006 reviews many of the commonly used measures in health research. Some current problems in measurement in health psychology research are the selection of methods to assess reliability (addressed by Sijtsma 2009), how to assess the content validity of theoretical constructs in a transparent manner (see Johnston, et al. 2014), and how to adapt measures for use in different languages and cultures (see Lopez-Roig and Pastor 2016).

• American Psychological Association. 2014. The revised “Standards for Educational and Psychological Testing”. American Psychological Association.

  An updated and revised version of the 1999 guidance. Particularly relevant when testing against a standard or norms.

• Fried, E. I., and J. K. Flake. 2020. Measurement matters. APS Observer 31:29–31.

  DOI: 10.17605/OSF.IO/ZRKD4

  Reviews measurement issues applicable to many areas of psychology, including psychometrics (especially reliability and validity), the challenge of scaling mental processes with no observable objective index, and reactivity of measures to the process of being measured.

• Grid-Enabled Measures Database (GEM).

  An interactive collaborative website providing a valuable, extensive database of measures used in the behavioral and social sciences. Supported by the US National Cancer Institute.

• Johnston, M., Y. Benyamini, and E. C. Karademas. 2016. Measurement Issues in Health Psychology. In Assessment in Health Psychology. Edited by Y. Benyamini, M. Johnston, and E. C. Karademas, 320–334. Göttingen, Germany: Hogrefe.

  Reviews current issues including links to theory and overlapping constructs, generic versus specific measures, idiographic and standard measures, constructing scales, response bias, context relevance, and acceptability and evaluation of measures.

• Johnston, M., D. Dixon, J. Hart, L. Glidewell, C. Schröder, and B. Pollard. 2014. Discriminant Content Validity (DCV): A quantitative methodology for assessing content of theory-based measures, with illustrative applications. British Journal of Health Psychology 19.2: 240–257.

  DOI: 10.1111/bjhp.12095

  A guide to the assessment of discriminant content validity to complement other forms of content validity.

• Lopez-Roig, S., and M.-A. Pastor. 2016. Cultural adaptation of measures. In Assessment in health psychology. Edited by Y. Benyamini, M. Johnston, and E. C. Karademas, 265–277. Göttingen, Germany: Hogrefe.

  An informative guide to making measures translate between cultures and languages.

• McDowell, I. 2006. Measuring health: A guide to rating scales and questionnaires. 3d ed. Oxford: Oxford Univ. Press.

  Describes many scales used in health research and provides psychometric data.

• Patient-Reported Outcomes Measurement Information System (PROMIS).

  An extensive compendium of measures for the evaluation and monitoring of health conditions. Produced and maintained by the US National Institutes of Health.

• Science of Behavior Change: Repository of Measures.

  An online resource produced by research funded by the US National Institutes of Health. Measures investigated are relevant to key domains for health psychology: self-regulation, stress reactivity and stress resilience, and interpersonal and social processes. Each measure has been or is being subject to a thorough process of validation and all information is available on the website.

• Sijtsma, K. 2009. On the use, the misuse, and the very limited usefulness of Cronbach’s Alpha. Psychometrika 74.1: 107–120.

  DOI: 10.1007/s11336-008-9101-0

  Reviews problems in using Cronbach’s alpha to assess internal consistency.

• Smith, T. W. 2011. Measurement in health psychology research. In The Oxford handbook of health psychology. Edited by H. F. Friedman, 42–72. Oxford: Oxford Univ. Press.

  Discusses measurement issues and the evaluation of measures. Discusses three domains in detail: stress and disease; health behavior and prevention; psychosocial aspects of medical illness and care.

• Streiner, D. L., R. Norman, and J. Cairney. 2015. Health measurement scales: A practical guide to their development and use. 5th ed. Oxford: Oxford Univ. Press.

  A good overview of methods and issues in the measurement of health, including sections on item selection and scale development, biases in responding, item response theory, reliability, validation, and administration of scales.